This article has been reviewed according to ScienceX's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: close In a review published in Engineering, scientists explore the burgeoning field of machine learning (ML) and its applications in chemistry. Titled "Machine Learning for Chemistry: Basics and Applications," this comprehensive review aims to bridge the gap between chemists and modern ML algorithms, providing insights into the potential of ML in revolutionizing chemical research

Apple may not be as flashy as other companies in adopting artificial intelligence features.

If you have a curiosity about how fancy graphics cards actually work, and why they are so well-suited to AI-type applications, then take a few minutes to read[Tim Dettmers] explain why this is so. Its not a terribly long read, but while it does get technical there are also car analogies, so theres something for everyone! He starts off by saying that most people know that GPUs are scarily efficient at matrix multiplication and convolution, but what really makes them most useful is their ability to work with large amounts of memory very efficiently. Essentially, a CPU is a latency-optimized device while GPUs are bandwidth-optimized devices.